Apparatus and method for analyzing body part association

ABSTRACT

An apparatus and method for analyzing body part association. The apparatus and method may recognize at least one body part from a user image extracted from an observed image, select at least one candidate body part based on association of the at least one body part, and output a user pose skeleton related to the user image based on the selected at least one candidate body part.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of Korean PatentApplication No. 10-2012-0004435, filed on Jan. 13, 2012, in the KoreanIntellectual Property Office, the disclosure of which is incorporatedherein by reference.

BACKGROUND

1. Field

Example embodiments of the following description relate to an apparatusand method for analyzing association of body parts in recognizing a poseof a user, and more particularly, an apparatus and method for analyzingassociation of body parts in recognizing a pose of a user by taking intoconsideration a figure of a user shown in an image input at the time ofrecognition of a human body.

2. Description of the Related Art

Usually, humans use two eyes to correctly recognize a pose of the humanbody, even at a long distance and/or a low definition.

A method for recognizing a human body pose using a computer visionsystem has recently been gaining demanded in various fields. However,accurate recognition of the human body pose is still a difficult matter.

Generally, a model-based method has been mainly used for recognition ofthe human body pose.

However, the model-based method is inappropriate for estimation ofcomplex poses performed in a sitting or lying state, such as, yogaposes, stretching, and the like, due to the occlusion of some bodyparts.

In addition, according to general body part recognition methods, errorsmay occur during the recognition when the same body part may berecognized in several different positions when a new non-learned bodyfigure is detected or various costumes are applied. Therefore,connection positions may not be accurately estimated.

Accordingly, there is a need for an improved body part recognitionmethod that increases recognition efficiency by taking intoconsideration a figure of a user shown in an image input at the time ofrecognition of a human body.

SUMMARY

The foregoing and/or other aspects are achieved by providing a body partassociation analysis apparatus including an image extraction unit toextract a user image from an observed image, a body recognition unit torecognize at least one body part from the user image, a body selectionunit to select at least one candidate body part based on association ofthe at least one body part, and a pose determination unit to output auser pose skeleton related to the user image based on the selected atleast one candidate body part.

The pose determination unit may include a connection structurerecognition unit to extract a body part recognition result inconsideration of a sequential connection structure of a human body basedon the at least one candidate body part, and a candidate bone generationunit to generate at least one candidate bone using the body partrecognition result.

The candidate bone generation unit may generate the at least onecandidate bone by defining at least one association group of correlatedcandidate body parts among the at least one candidate body part.

The candidate bone generation unit may generate the candidate bone byconnecting adjacent candidate body parts with respect to a bodystructure, selected from the at least one association group.

The pose determination unit may further include a misrecognitionanalysis unit to analyze a misrecognition result with respect to the atleast one candidate bone.

The pose determination unit may further include a connection positionanalysis unit to extract connection positions with respect to the bodyparts in consideration of the misrecognition result.

The pose determination unit may restore current skeleton informationconnected to skeleton information of a previous image frame, byreflecting the body part recognition result.

The pose determination unit may include a seed selection unit to selecta seed body part connected to a torso from the at least one body part,an allocation unit to allocate the seed body part as a base body part, aconnection search unit to search for a connected body part which isconnected to the base body part based on the body structure, from the atleast one candidate body part, a comparison unit to calculate a depthcontinuity that connects the base body part with the connected body partand to compare the depth continuity with a preset value, and a candidatedeletion unit to delete the selected candidate body part when the depthcontinuity is less than or equal to the preset value. When the connectedbody part is not a terminal body part, the connection search unit maysearch again for the connected body part by allocating the connectedbody part as the base body part.

The pose determination unit may include a both end selection unit toselect both-end body parts corresponding to both ends of the at leastone candidate bone, a matching determination unit to calculate an ACCvalue (Accurate Value) by determining a matching degree between a lineinterconnecting the both-end body parts and a silhouette of the userimage, a candidate deletion unit to delete the at least one candidatebone when the ACC value is less than or equal to a preset threshold orless than or equal to a currently stored maximum ACC value, and an ACCstorage unit to store the calculated ACC value as a maximum ACC valuewhen the ACC value is greater than the preset threshold or greater thanthe currently stored maximum ACC value. The pose determination unit mayselect the at least one candidate bone by repeating operations of theforegoing units.

The pose determination unit may include a both end selection unit toselect both-end body parts corresponding to both ends of the at leastone candidate bone, a first candidate deletion unit to delete the atleast one candidate bone when a length of a line interconnecting theboth-end both parts is not in a range between a maximum model length anda minimum model length stored in a human body model, and a secondcandidate deletion unit to determine whether the at least one candidatebone has a possible angle for an actual body structure, and to deletethe at least one candidate bone when the at least one candidate bonedoes not have the possible angle. The pose determination unit may selectthe at least one candidate bone by repeating operations of the foregoingunits.

The foregoing and/or other aspects are achieved by providing a body partassociation analysis method including extracting a user image from anobserved image, recognizing at least one body part from the user image,selecting at least one candidate body part based on association of theat least one body part, and outputting a user pose skeleton related tothe user image based on the selected at least one candidate body part.

The foregoing and/or other aspects are achieved by providing a methodfor increasing recognition efficiency, including selecting pluralcandidate body parts from a user image; generating at least onecandidate bone by associating the plural candidate body parts; analyzingthe associated plural candidate body parts; and determining amisrecognition result to be filtered off, based on the analyzing.

Additional aspects, features, and/or advantages of example embodimentswill be set forth in part in the description which follows and, in part,will be apparent from the description, or may be learned by practice ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects and advantages will become apparent and morereadily appreciated from the following description of the exampleembodiments, taken in conjunction with the accompanying drawings ofwhich:

FIG. 1 is a block diagram illustrating a body part association analysisapparatus, according to example embodiments;

FIG. 2 is a flowchart illustrating a body part association analysismethod, according to example embodiments;

FIG. 3 is a diagram illustrating a body part association analysis methodusing association of body parts, according to example embodiments;

FIGS. 4 and 5 are diagrams illustrating candidate body parts selected byan operation of generating a candidate bone, according to exampleembodiments;

FIG. 6 is a diagram illustrating a candidate bone, according to exampleembodiments;

FIG. 7 is a flowchart illustrating a sequential alignment testingmethod, according to example embodiments;

FIG. 8 is a flowchart illustrating a silhouette testing method,according to example embodiments;

FIG. 9 is a flowchart illustrating a bone length and angle testingmethod, according to example embodiments

FIG. 10 is a block diagram illustrating a pose determination unit,according to example embodiments; and

FIG. 11 is a block diagram illustrating additional units of the posedetermination unit, according to example embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to the like elementsthroughout.

In the description of the present invention, if detailed descriptions ofrelated disclosed art or configuration are determined to unnecessarilymake the subject matter of the present invention obscure, they will beomitted. Terms to be used below are defined based on their functions inthe present invention and may vary according to users, user'sintentions, or practices. Therefore, the definitions of the terms shouldbe determined based on the entire specification.

A body part association analysis apparatus according to exampleembodiments may increase recognition efficiency of human poses by takinginto consideration a figure of a user shown in an image input at thetime of recognition of a human body.

For example, body parts including a head, a neck, shoulders, elbows, atorso, hands, knees, legs, and the like, may be recognized from an inputuser image. Next, a misrecognition result may be filtered off byanalyzing association of the body parts from a result of therecognition.

FIG. 1 illustrates a body part association analysis apparatus 100,according to example embodiments.

Referring to FIG. 1, the body part association analysis apparatus 100may include an image extraction unit 110 that extracts a user image froman observed image, a body recognition unit 120 that recognizes at leastone body part from the user image, a body selection unit 130 thatselects at least one candidate body part based on association of the atleast one body part, and a pose determination unit 140 that outputs auser pose skeleton related to the user image based on the selected atleast one candidate body part.

Hereinafter, a method of analyzing body part association using the bodypart association analysis apparatus 100 of FIG. 1 will be described. Forunderstanding of the example embodiments, reference numerals for therespective units shown in FIG. 1 will be cited throughout.

FIG. 2 illustrates a body part association analysis method, according toexample embodiments.

Referring to FIG. 2, the body part association analysis apparatus 100extracts a user image from an observed image by the image extractionunit 110, and recognizes at least one body part from the user image bythe body recognition unit 120, in operation 210.

In operation 210, the body part association analysis apparatus 100 maylearn a background of the observed image of a photographing devicethrough background learning, and output, as the user image, a differenceimage showing a difference between the learned background and a currentimage. However, a method for performing operation 210 is notspecifically limited thereto.

The body part association analysis apparatus 100 may select at least onecandidate body from the recognized at least one body part, using a bodyselection unit 130, in operation 220.

That is, in operation 220, the body part association analysis apparatus100 may select the at least one candidate body part from the user imageby body part classifiers (BPCL) learned through various machine learningmethods, for example, adaptive boosting, random forest, decision tree,neural network, and the like.

The body part association analysis apparatus 100 may output a user poseskeleton related to the user image, based on the selected at least onecandidate body part using a pose determination unit 140, in operation230.

In operation 230, the position association analysis apparatus 100 maygenerate a candidate bone by inputting the at least one candidate bodypart, and thus, may finally output the final user pose skeleton byinspecting a misrecognition result.

FIG. 3 illustrates a position association analysis method usingassociation of body parts, according to example embodiments.

Referring to FIG. 3, in operation 310, the pose determination unit 140may extract a body part recognition result by a connection structurerecognition unit 140A (refer to FIGS. 10 and 11) in consideration of asequential connection structure of a human body based on at least onecandidate body part.

For example, the pose determination unit 140 may perform sequentialalignment testing by inputting an image corresponding to the at leastone body part, in extracting the body part recognition result.

In operation 320, the pose determination unit 140 may generate at leastone candidate bone by a candidate bone generation unit 140B (refer toFIGS. 10 and 11) using the body part recognition result.

The candidate bone generation unit 140B may generate the at least onecandidate bone by defining at least one association group of correlatedcandidate body parts selected from the at least one candidate body part.

The candidate bone generation unit 140B may generate the at least onecandidate bone by connecting adjacent candidate body parts with respectto a body structure, selected from the at least one association group.

According to the example embodiments, various associations of the atleast one candidate body parts may be generated. For example, togenerate an association of the correlated candidate body parts fromamong the various associations, the candidate body parts may beclassified into a plurality of groups, as shown in Table 1, for example.Thus, the at least one candidate bone may be generated.

TABLE 1 Group Classification Torso group Head, neck, spine, pelvis Leftarm group Left shoulder, left elbow, left wrist, left hand Right armgroup Right shoulder, right elbow, right wrist, right hand Left leggroup Left thigh, left knee, left ankle, left foot Right leg group Rightthigh, right knee, right ankle, right foot

According to the example embodiments, the at least one candidate bonemay be generated by connecting the adjacent candidate body parts withrespect to the body structure from among the candidate body partsincluded in the respective groups.

The pose determination unit 140 may analyze a misrecognition result withrespect to the at least one candidate bone, by a misrecognition analysisunit 140C (refer to FIGS. 10 and 11), in operation 330.

The body part association analysis apparatus 100 may detect themisrecognition result, for example, by sequential alignment testing,silhouette testing, bone length and angle testing, and the like.

The foregoing misrecognition result detection methods may be appliedtogether or separately, and will be described in detail hereinafter.

A result of reflecting the misrecognition result detection methods maybe used as an input of a Bayesian Estimation Framework, as expressed byEquation 1 below.

P(X|Z)=max(P(X|X′)P(X′|Z′))P(Z|X)P(X)   [Equation 1]

Here, P(X|Z) denotes a prediction probability of connection between abody part Z and a body part X, P(X|X′) denotes a probability ofconversion from a body part X′ to the body part X, P(X′|Z′) denotes afinal connection estimation probability, P(Z|X) denotes an X connectionprobability according to a body part recognition probability, and P(X)denotes a model according to the X connection probability.

In operation 340, the pose determination unit 140 may extract connectionpositions with respect to the body parts in consideration of themisrecognition result, by a connection position analysis unit 140D(refer to FIGS. 10 and 11).

When the recognition result about the candidate body parts is notpruned, a plurality of associations of the candidate body parts may begenerated, accordingly causing some inaccurate skeletons. Therefore, theconnection positions may be accurately recognized at the respective bodyparts by detected the misrecognition result, and having themisrecognition result filtered off.

In operation 350, the pose determination unit 140 may restore currentskeleton information connected to skeleton information of a previousimage frame, by reflecting the body part recognition result.

FIGS. 4 and 5 illustrate candidate body parts selected by operation 220,according to example embodiments.

Referring to FIGS. 4 and 5, a pose determination unit 140 may output aresult that may be recognized as an elbow BP1 or a hand BP2 byassociation of learned candidate body parts.

FIG. 6 illustrates a candidate bone, according to example embodiments.

Referring to FIG. 6, a pose determination unit 140, according to theexample embodiments, may output three candidate body parts, for example,which may constitute a bone when connected with an elbow and a hand, andselect any proper candidate body part from the three candidate bodyparts. Although this example shows that three candidate body parts areoutput, the present disclosure is not limited thereto.

The body part association analysis apparatus may recognize body parts onthe basis of sequential alignment testing, and select proper candidatebody parts by selecting adjacent body parts with respect to a bodystructure so as to delete a misrecognition result about the body partsfrom a user image of a user in various figures.

FIG. 7 illustrates an exemplary sequential alignment testing method,according to example embodiments.

Referring to FIG. 7, in operation 710, a pose determination unit 140 mayselect a seed body part connected to a torso from at least one bodypart, using a seed selection unit 140E.

For example, the seed selection unit 140E (refer to FIGS. 10 and 11) mayselect a seed body part S connected to the torso from an image of atleast one recognized body parts. The seed body part S may include aneck, a shoulder, a pelvis, and the like.

In operation 720, the pose determination unit 140 may allocate the seedbody part S as a base body part using an allocation unit 140F (refer toFIGS. 10 and 11).

For example, the allocation unit 140F may allocate the seed body part Sas the base body part Cbase.

In operation 730, the pose determination unit 140 may search for aconnected body part connected to the base body part with respect to abody structure, from the at least one candidate body part, using aconnection search unit 140G (refer to FIGS. 10 and 11).

The pose determination unit 140 may search for a connected body part Cthat may be connected to the base body part Cbase based on the bodystructure. For example, the connected body part of the neck may be ahead, and the connected body part of the shoulders may be upper arms.

In operation 740, the pose determination unit 140 may calculate a depthcontinuity of a line that connects the base body part with the connectedbody part and may compare the depth continuity with a preset value,using a comparison unit 140H (refer to FIGS. 10 and 11).

For example, the comparison unit 140H may calculate the depth continuityof an imaginary line connecting the base body part Cbase with theconnected body part C.

In operation 750, the pose determination unit 140 may delete theselected candidate body part when the depth continuity is less than orequal to the preset value, using a candidate deletion unit 1401 (referto FIGS. 10 and 11).

For example, when the depth continuity is less than or equal to a presetthreshold, the candidate deletion unit 1401 may consider the selectedcandidate body part to be a misrecognition, and thus, the selectedcandidate body part is not the connected body part C, and subsequentlydelete the connected body part C from the at least one candidate bodypart.

In operation 760, the pose determination unit 140 may determine whetherthe connected body part is a terminal body part, by the connectionsearch unit 140G.

In operation 770, when the connected body part is the terminal bodypart, the connection search unit 140G may allocate the connected bodypart C as the base body part.

For example, when the connected body part C is not the terminal bodypart (examples of the terminal body part being hands, feet, and a headbased on the body structure), the connection search unit 140G mayallocate the connected body part C as the base body part Cbase, therebycontinuously searching for the connected body part C based on the bodystructure.

In operation 780, when the connected body part is not the terminal bodypart, the connection search unit 140G may determine presence of anotherseed body part and search again for the connected body part byallocating the connected body part as the base body part.

The body part association analysis apparatus 100 may recognize the bodyparts on the basis of the silhouette testing. Also, to delete amisrecognition result about the body parts from a user image of a userin various figures, the body part association analysis apparatus 100 mayselect a recognition result by determining whether a silhouette imagematches a candidate bone.

FIG. 8 illustrates an exemplary silhouette testing method according toexample embodiments.

Referring to FIG. 8, in operation 810, a pose determination unit 140 mayselect both-end body parts corresponding to both ends of at least onecandidate bone, by a both end selection unit 140J.

For example, the both end selection unit 140J (refer to FIGS. 10 and 11)may perform the silhouette testing to extract a bone connecting an elbowBP1 and a hand BP2, and select body parts BP1 and BP2 disposed at bothends of a bone of the at least one candidate bone.

According to the body part recognition result, at least one result maybe derived from the body parts BP1 and BP2 as shown in FIG. 4.

In operation 820, the pose determination unit 140 may calculate an ACCvalue (Accurate Value) by determining a matching degree between a lineinterconnecting the both-end body parts and a silhouette of the userimage, by a matching determination unit 140K (refer to FIGS. 10 and 11).

For example, the matching determination unit 140K may detect, from theACC value, the matching degree between the line interconnecting the bodyparts BP1 and BP2 and the silhouette of the user image.

In operation 830, the pose determination unit 140 may determine whetherthe ACC value is less than or equal to a preset threshold or less thanor equal to a currently stored maximum ACC value (CURRENT_MAX). When theACC value is less than the preset value or the CURRENT_MAX, the posedetermination unit 140 may delete the at least one candidate bone by acandidate deletion unit 1401 in operation 840.

In operation 850, the pose determination unit 140 may determine whetherthe ACC value is greater than the preset threshold or greater than theCURRENT_MAX. When the ACC value is greater than the preset threshold orthe CURRENT_MAX, the pose determination unit 140 may store the ACC valueas a maximum ACC value in operation 860.

The pose determination unit 140 may determine whether another candidatebone exists in operation 870, and select the at least one candidate boneby repeating the foregoing operations of the respective units, therebyfinally outputting a user pose skeleton in operation 880.

The body part association analysis apparatus 100 may recognize the atleast one body part based on the bone length and angle testing. Todelete a misrecognition result about the body parts from a user image ofa user in various figures, the body part association analysis apparatus100 may select a recognition result by determining whether a length andan angle of the at least one candidate bone is similar to those of askeleton model.

FIG. 9 illustrates an exemplary bone length and angle testing method,according to example embodiments.

Referring to FIG. 9, a pose determination unit 140 may select both-endbody parts with respect to at least one candidate bone, using a both endselection unit 140J, in operation 910.

For example, the both end selection unit 140J may perform silhouettetesting to extract a bone connecting an elbow and a hand, and selectbody parts BP1 and BP2 disposed at both ends of a bone of the at leastone candidate bone.

According to the body part recognition result, at least one result maybe derived from the body parts BP1 and BP2 as shown in FIG. 4.

The pose determination unit 140 may calculate a length of a lineinterconnecting the both-end body parts in operation 920.

Using a first candidate deletion unit 140L (refer to FIGS. 10 and 11),the pose determination unit 140 may determine whether the length of theline interconnecting the both-end body parts is in a range between amaximum model length and a minimum model length stored in a human bodymodel in operation 930, and, when the length of the line is not in therange, may delete the at least one candidate bone in operation 940.

For example, when the length of the line interconnecting the selectedbody parts BP1 and BP2 is not in the range between a maximum lengthMODEL_MAX and a minimum length MODEL_MIN read from the human body model,the first candidate deletion unit 140L may consider the correspondingcandidate bone as a misrecognized candidate bone and delete thecorresponding candidate bone.

In operation 950, when the length of the line is in the range, the posedetermination unit 140 may extract an angle of the at least onecandidate bone.

In operation 960, the pose determination unit 140 may determine whetherthe at least one candidate bone has a possible angle for an actual bodystructure using a second candidate deletion unit 140M (refer to FIGS. 10and 11). In operation 940, the pose determination unit 140 may deletethe at least one candidate bone when the at least one candidate bonedoes not have the possible angle using the second candidate deletionunit 140M.

When the at least one candidate bone has the possible angle, the posedetermination unit 140 may store the at least one candidate bone inoperation 970.

The pose determination unit 140 may determine whether another candidatebone exists in operation 980. In addition, the pose determination unit140 may select the at least one candidate bone by repeating theforegoing operations of the respective units until no more candidatebone remains, thereby finally outputting a user pose skeleton, inoperation 990.

FIGS. 10 and 11 are block diagrams illustrating the various unitsincluded in the pose determination unit 140.

According to the example embodiments, a pose of a user in a newnon-learned figure which is not used in user image learning may berecognized.

In addition, a pose of a user in a new costume may be recognized.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations embodied by a computer. Themedia may also include, alone or in association with the programinstructions, data files, data structures, and the like. The programinstructions recorded on the media may be those specially designed andconstructed for the purposes of the example embodiments, or they may beof the kind well-known and available to those having skill in thecomputer software arts.

The embodiments can be implemented in computing hardware (computingapparatus) and/or software, such as (in a non-limiting example) anycomputer that can store, retrieve, process and/or output data and/orcommunicate with other computers. The results produced can be displayedon a display of the computing hardware. A program/software implementingthe embodiments may be recorded on non-transitory computer-readablemedia comprising computer-readable recording media. Examples ofnon-transitory computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such as CDROM disks and DVDs; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory (ROM), random accessmemory (RAM), flash memory, and the like. The media may be transfermedia such as optical lines, metal lines, or waveguides including acarrier wave for transmitting a signal designating the program commandand the data construction. Examples of program instructions include bothmachine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter. Examples of the magnetic recording apparatus include a harddisk device (HDD), a flexible disk (FD), and a magnetic tape (MT).Examples of the optical disk include a DVD (Digital Versatile Disc), aDVD-RAM, a CD-ROM (Compact Disc-Read Only Memory), and a CD-R(Recordable)/RW. The described hardware devices may be configured to actas one or more software modules in order to perform the operations ofthe above-described example embodiments, or vice versa.

Further, according to an aspect of the embodiments, any combinations ofthe described features, functions and/or operations can be provided.

Moreover, the body part association analysis apparatus may include atleast one processor to execute at least one of the above-described unitsand methods.

Although example embodiments have been shown and described, it would beappreciated by those skilled in the art that changes may be made inthese example embodiments without departing from the principles andspirit of the disclosure, the scope of which is defined in the claimsand their equivalents.

What is claimed is:
 1. A body part association analysis apparatus,comprising: an image extraction unit to extract a user image from anobserved image; a body recognition unit to recognize at least one bodypart from the extracted user image; a body selection unit to select atleast one candidate body part from the extracted user image based on therecognized at least one body part; and a pose determination unit tooutput a user pose skeleton related to the extracted user image based onthe selected at least one candidate body part.
 2. The body partassociation analysis apparatus of claim 1, wherein the posedetermination unit comprises: a connection structure recognition unit toextract a body part recognition result based on a sequential connectionstructure of a human body using the selected at least one candidate bodypart; and a candidate bone generation unit to generate at least onecandidate bone using the body part recognition result.
 3. The body partassociation analysis apparatus of claim 1, wherein the user imagecomprises a difference image showing a difference between a backgroundof the observed image and a current image.
 4. The body part associationanalysis apparatus of claim 2, wherein the candidate bone generationunit generates the at least one candidate bone by defining at least oneassociation group of correlated candidate body parts among the at leastone candidate body part.
 5. The body part association analysis apparatusof claim 4, wherein the candidate bone generation unit generates thecandidate bone by connecting adjacent candidate body parts with respectto a body structure, selected from the at least one association group.6. The body part association analysis apparatus of claim 4, wherein thepose determination unit further comprises a misrecognition analysis unitto analyze a misrecognition result with respect to the generated atleast one candidate bone.
 7. The body part association analysisapparatus of claim 6, wherein the misrecognition result is detectedusing at least one of sequential alignment testing, silhouette testing,and bone length and angle testing.
 8. The body part association analysisapparatus of claim 6, wherein the pose determination unit furthercomprises a connection position analysis unit to extract connectionpositions with respect to the selected at least one candidate body partbased on the misrecognition result.
 9. The body part associationanalysis apparatus of claim 2, wherein the pose determination unitrestores current skeleton information connected to skeleton informationof a previous image frame, by reflecting the extracted body partrecognition result.
 10. The body part association analysis apparatus ofclaim 1, wherein the pose determination unit comprises: a seed selectionunit to select a seed body part, from the at least one candidate bodypart, connected to a torso; an allocation unit to allocate the seed bodypart as a base body part; a connection search unit to search for aconnected body part which is connected to the base body part based on abody structure, from the at least one candidate body part; a comparisonunit to calculate a depth continuity of a line that connects the basebody part with the connected body part and to compare the depthcontinuity with a preset value; and a candidate deletion unit to deletethe selected at least one candidate body part when the depth continuityis less than or equal to the preset value, and wherein, when theconnected body part is not a terminal body part, the connection searchunit continues to search for the connected body part by allocating theconnected body part as the base body part.
 11. The body part associationanalysis apparatus of claim 2, wherein the pose determination unitcomprises: a both end selection unit to select both-end body partscorresponding to both ends of the at least one candidate bone; amatching determination unit to calculate an ACC value (Accurate Value)by determining a matching degree between a line interconnecting theboth-end body parts and a silhouette of the user image; a candidatedeletion unit to delete the at least one candidate bone when the ACCvalue is less than or equal to a preset threshold or less than or equalto a currently stored maximum ACC value; and an ACC storage unit tostore the calculated ACC value as a maximum ACC value when thecalculated ACC value is greater than the preset threshold or greaterthan the currently stored maximum ACC value, and wherein the posedetermination unit determines whether another candidate bone exists, andthe outputting of the user pose skeleton selects the at least onecandidate bone.
 12. The body part association analysis apparatus ofclaim 2, wherein the pose determination unit comprises: a both endselection unit to select both-end body parts corresponding to both endsof the at least one candidate bone; a first candidate deletion unit todelete the at least one candidate bone when a length of a lineinterconnecting the both-end body parts is not in a range between amaximum model length and a minimum model length stored in a human bodymodel; and a second candidate deletion unit to determine whether the atleast one candidate bone has a possible angle for an actual bodystructure, and to delete the at least one candidate bone when the atleast one candidate bone does not have the possible angle, wherein thepose determination unit determines whether another candidate boneexists, and the outputting of the user pose skeleton selects the atleast one candidate bone.
 13. A body part association analysis methodcomprising: extracting a user image from an observed image; recognizingat least one body part from the extracted user image; selecting at leastone candidate body part from the extracted user image based on the atleast one body part; and outputting a user pose skeleton related to theextracted user image based on the selected at least one candidate bodypart.
 14. The body part association analysis method of claim 13, whereinthe outputting of the user pose skeleton comprises: extracting a bodypart recognition result based on a sequential connection structure of ahuman body using the at least one candidate body part; and generating atleast one candidate bone using the body part recognition result.
 15. Thebody part association analysis method of claim 14, wherein thegenerating of the at least one candidate bone comprises: generating atleast one candidate bone by defining at least one association group ofcorrelated candidate body parts among the at least one candidate bodypart.
 16. The body part association analysis method of claim 15, whereinthe generating of the at least one candidate bone comprises: generatingthe candidate bone by connecting adjacent candidate body parts withrespect to a body structure, selected from the at least one associationgroup.
 17. The body part association analysis method of claim 15,wherein the generating of the at least one candidate bone furthercomprises: analyzing a misrecognition result with respect to the atleast one candidate bone.
 18. The body part association analysis methodof claim 17, wherein the generating of the at least one candidate bonefurther comprises: extracting connection positions with respect to theat least one candidate body part based on the misrecognition result. 19.The body part association analysis method of claim 14, wherein theoutputting of the user pose skeleton comprises: restoring currentskeleton information connected to skeleton information of a previousimage frame, by reflecting the body part recognition result.
 20. Thebody part association analysis method of claim 13, wherein theoutputting of the user pose skeleton comprises: selecting a seed bodypart, from the at least one candidate body part, connected to a torso;allocating the seed body part as a base body part; searching for aconnected body part which is connected to the base body part based onthe body structure, from the at least one candidate body part;calculating a depth continuity of a line that connects the base bodypart with the connected body part and to compare the depth continuitywith a preset value; deleting the selected at least one candidate bodypart when the depth continuity is less than or equal to the presetvalue; and searching again for the connected body part by allocating theconnected body part as the base body part when the connected body partis not a terminal body part.
 21. The body part association analysismethod of claim 14, wherein the outputting of the user pose skeletoncomprises: selecting both-end body parts corresponding to both ends ofthe at least one candidate bone; calculating an ACC value (AccurateValue) by determining a matching degree between a line interconnectingthe both-end body parts and a silhouette of the user image; deleting theat least one candidate bone when the calculated ACC value is less thanor equal to a preset threshold or is less than or equal to a currentlystored maximum ACC value; and storing the calculated ACC value as amaximum ACC value when the calculated ACC value is greater than thepreset threshold or is greater than the currently stored maximum ACCvalue, and wherein whether another candidate bone exists is determined,and the outputting of the user pose skeleton selects the at least onecandidate bone .
 22. The body part association analysis method of claim14, wherein the outputting of the user pose skeleton comprises:selecting both-end body parts corresponding to both ends of the at leastone candidate bone; deleting the at least one candidate bone when alength of a line interconnecting the both-end both parts is not in arange between a maximum model length and a minimum model length storedin a human body model; determining whether the at least one candidatebone has a possible angle for an actual body structure, and deleting theat least one candidate bone when the at least one candidate bone doesnot have the possible angle, and wherein whether another candidate boneexists is determined, and the outputting of the user pose skeletonselects the at least one candidate bone.
 23. A non-transitory computerreadable recording medium storing a program to cause a computer toimplement the method of claim
 13. 24. A method for increasingrecognition efficiency, comprising: selecting plural candidate bodyparts from a user image; generating at least one candidate bone byassociating the plural candidate body parts; analyzing the associatedplural candidate body parts for a misrecognition result; and determiningthe misrecognition result to be filtered off, based on the analyzing.